A rare disease pharmaceutical manufacturer partnered with Capital S Consulting to build an AI lead scoring system to identify patients earlier in their treatment journey. This improved treatment outcomes while identifying $30 million in incremental revenue opportunities and moved their commercial teams from static territory lists to precision patient identification.

Here's how the system worked and the results it delivered over three years in operation.

The Old Way: Guessing Where Patients Might Be

Before AI lead scoring, this manufacturer worked like most pharmaceutical companies. Territory managers received static target lists based on healthcare provider specialty and practice size. A healthcare provider with lots of patients received priority attention, whether or not they currently had patients with the specific condition.

The typical approach meant territory managers worked through static lists, sorted by geography and practice size, trying to meet with healthcare providers based on whatever metrics they had available. They couldn't tell which healthcare providers were treating patients with their target condition.

This created inefficiency throughout the process. Teams spent time educating healthcare providers about rare diseases without knowing if those healthcare providers had relevant patients. Meanwhile, patients with positive diagnostic markers remained invisible until worsening symptoms triggered specialized testing.

Lab and claims data contained valuable patient signals, but no system connected these insights to commercial outreach. Test results showing specific conditions went unused by the teams who could act on them.

The AI Solution: Learning Through Iteration

Capital S Consulting built a system that combined multiple data sources using tokenization technology to track patient journeys across different platforms. The initial approach focused on obvious signals - specific diagnostic codes and lab test results that medical teams knew indicated the target condition.

The breakthrough came through iterative learning. When laboratory data from providers like Prognos showed positive test results, the AI system automatically created opportunities tied to appropriate prescribing healthcare providers. As territory managers worked these leads and reported outcomes, the system learned which combinations of signals predicted successful patient identification.

Over time, the AI uncovered patterns that human analysis missed. Certain procedure codes, when combined with specific claims activity, indicated higher probability than obvious diagnostic markers alone. This enabled the system to identify patients earlier in their healthcare journey, often before traditional diagnostic approaches would have flagged them. The system kept refining its understanding of which signals most accurately predicted patient needs, becoming more effective with each iteration.

Rather than static target lists, commercial teams received dynamic opportunities linked to specific patient profiles. Territory managers could contact healthcare providers about patients showing certain diagnostic patterns instead of broad disease education outreach.

The AI system processed claims data, lab results, and healthcare provider interaction history to identify the most appropriate healthcare provider for each patient profile. Machine learning algorithms determined whether a specialist or primary care provider should receive the lead based on recent treatment patterns and conversion success rates.

Implementation: From Manual to Automated

Before this system, teams had to manually review huge data files containing thousands of diagnostic codes and test results, then determine if patients qualified based on predetermined signals.

Capital S built automated workflows that handled this parsing and qualification process. Multiple data vendors fed information through secure channels that protected patient identity while allowing teams to see meaningful patterns across different sources.

Machine learning algorithms spotted patterns humans would miss while processing volumes that no manual process could handle. When new lab results showed positive tests, the system immediately generated alerts for territory managers. Instead of weekly or monthly data reviews, teams received near real-time notifications about patients needing attention.

Patient-First Results: Earlier Detection, Better Outcomes

The most important impact was on patients. Traditional pharmaceutical outreach often meant patients waited months for an accurate diagnosis as symptoms worsened. The AI system spotted patients through lab signals before conditions advanced to severe stages.

Commercial teams could now approach healthcare providers with relevant patient context: test results suggesting a condition that needed specialized treatment. Instead of generic disease education, conversations focused on diagnostic patterns healthcare providers were already seeing.

Territory assignments also became dynamic. Rather than static geographic boundaries, the system balanced patient opportunities across the commercial team based on where signals appeared. This ensured every qualified patient received attention.

This created better outcomes across the board. Patients received earlier intervention when treatments are most effective. Healthcare providers gained clinical intelligence that helped them make better care decisions. The pharmaceutical company identified significant revenue opportunities while helping connect patients with therapies they needed.

Results That Improved Over Time

Over three years, the system uncovered more than $30 million in incremental revenue opportunities. More importantly for long-term success, patients were diagnosed earlier than traditional approaches would have allowed.

Performance improved dramatically over time. The system identified $30 million in opportunities across three years, with over half of all leads based on early diagnostic signals identified in just the last six months as the AI became more precise.

Territory managers dramatically increased their efficiency. Teams spend less time on broad educational outreach and more time on targeted patient discussions with healthcare providers. This precision targeting increased conversion rates while cutting overall time from identification to treatment.

Beyond Pharmaceuticals: Getting Started

The principles applied here work across industries with complex sales processes and high-value prospects. The components stay consistent: multiple data sources, pattern recognition, continuous learning, and automated alerts. Industries with longer sales cycles and high-value transactions gain the greatest benefit from AI-driven prospect identification.

AI lead scoring works best when organizations already have trusted data sources and a team ready to act on better insights. Most organizations that already purchase market data have the foundation required to launch. The key is connecting those data sources, using AI to spot patterns not visible before, then turning those insights into actions teams can take.

Curious how AI lead scoring could work with your existing data? Contact Capital S Consulting to discuss what's possible.